Question Answering from Lecture Videos Based on Automatically-Generated Learning Objects

Abstract

In the past decade, we have witnessed a dramatic increase in the availability of online academic lecture videos. There are technical problems in the use of recorded lectures for learning: the problem of easy access to the multimedia lecture video content and the problem of finding the semantically appropriate information very quickly. The retrieval of audiovisual lecture recordings is a complex task comprising many objects. In our solution, speech recognition is applied to create a tentative and deficient transcription of the lecture video recordings. The transcription and the words from the power point slides are sufficient to generate semantic metadata serialized in an OWL file. Each video segment (the lecturer is speaking about one power point slide) represent a learning object. A question-answering system based on these learning objects is presented. The annotation process is discussed, evaluated and compared to a perfectly annotated OWL file and, further, to an annotation based on a corrected transcript of the lecture. Furthermore, the consideration of the chronological order of the learning objects leads to a better MRRvalue. Our approach out-performs the Google Desktop Search based on the question keywords.